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Found 13 papers, 1 papers with code
OpenCog Hyperon: A Framework for AGI at the Human Level and Beyond
An introduction to the OpenCog Hyperon framework for Artificiai General Intelligence is presented.
A meta-probabilistic-programming language for bisimulation of probabilistic and non-well-founded type systems
We introduce a formal meta-language for probabilistic programming, capable of expressing both programs and the type systems in which they are embedded.
GoodPoint: unsupervised learning of keypoint detection and description
This paper introduces a new algorithm for unsupervised learning of keypoint detectors and descriptors, which demonstrates fast convergence and good performance across different datasets.
Differentiable Probabilistic Logic Networks
Probabilistic logic reasoning is a central component of such cognitive architectures as OpenCog.
Improving Deep Models of Person Re-identification for Cross-Dataset Usage
Person re-identification (Re-ID) is the task of matching humans across cameras with non-overlapping views that has important applications in visual surveillance.
Metric Embedding Autoencoders for Unsupervised Cross-Dataset Transfer Learning
Cross-dataset transfer learning is an important problem in person re-identification (Re-ID).
Vision System for AGI: Problems and Directions
What frameworks and architectures are necessary to create a vision system for AGI?
Semantic Image Retrieval by Uniting Deep Neural Networks and Cognitive Architectures
Image and video retrieval by their semantic content has been an important and challenging task for years, because it ultimately requires bridging the symbolic/subsymbolic gap.
A Step from Probabilistic Programming to Cognitive Architectures
Probabilistic programming is considered as a framework, in which basic components of cognitive architectures can be represented in unified and elegant fashion.
Universal Empathy and Ethical Bias for Artificial General Intelligence
We assume that generalized states of the world are valuable - not rewards themselves, and propose an extension of AIXI, in which rewards are used only to bootstrap hierarchical value learning.
Direct Uncertainty Estimation in Reinforcement Learning
Optimal probabilistic approach in reinforcement learning is computationally infeasible.
Extending Universal Intelligence Models with Formal Notion of Representation
Solomonoff induction is known to be universal, but incomputable.
Universal Induction with Varying Sets of Combinators
Our experiments show that low-complexity induction or prediction tasks can be solved by the developed system (much more efficiently than using brute force); useful combinators can be revealed and included into the representation simplifying more difficult tasks.
